{"@context":"https://schema.org","@type":"JobPosting","title":"MLOps Intern","description":"

📝 MLOps Intern (Unpaid)

⚠️ Applications will only be accepted through the following site: 👉 www.owlera.site

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About Owlera

Owlera is an AI-powered career platform that helps students and fresh graduates gain practical experience through internship simulations inspired by real companies.

Participants work on realistic business challenges, receive AI-powered feedback, use modern professional tools, and build portfolio-ready projects that demonstrate their abilities to future employers.

We're looking for curious, analytical, and ambitious students to join our MLOps Internship and gain hands-on experience with the engineering behind real AI products — how machine-learning models are trained, deployed, monitored, and kept reliable in production.

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About the Internship

As a MLOps Intern, you'll work on realistic challenges centered on machine-learning operations — taking models from a notebook to production, automating training and deployment pipelines, and keeping AI systems fast, reproducible, and reliable.

You'll learn how AI teams ship and operate models at scale — experiment tracking, CI/CD for ML, model registries, serving infrastructure, monitoring, and the automation that makes AI products dependable.

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Responsibilities

Set up experiment tracking and version control for datasets, models, and code.
Build training pipelines that are reproducible and automated.
Package models with Docker and deploy them as REST APIs.
Implement model registries and versioning (which model is live, and why).
Automate model validation and testing before deployment.
Set up CI/CD pipelines for ML workflows.
Monitor deployed models — latency, error rates, and prediction drift.
Implement data validation checks to catch bad data before it breaks models.
Build automated retraining triggers and pipelines.
Work with feature stores and data pipelines for training and serving.
Optimize inference performance — batching, caching, and hardware tradeoffs.
Implement rollback strategies and safe deployment patterns (canary, blue-green).
Create dashboards that surface model health and pipeline status.
Write runbooks and documentation for production ML workflows.
Collaborate with other interns on cross-functional AI projects.
Translate real-world product requirements into reliable ML systems.

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Requirements

Current student or recent graduate in Computer Science, AI, Data Science, or a related field — or a strong self-taught coding portfolio.
Strong interest in machine learning systems and how models run in production.
Basic programming skills (Python is a plus).
Familiarity with Git and command-line basics.
Some exposure to machine learning concepts (training, evaluation, inference).
Analytical and logical thinking with close attention to detail.
Self-motivated and able to work independently on technical challenges.
Comfortable experimenting, failing fast, and iterating.
Familiarity with AI tools such as ChatGPT, Claude, Gemini, or similar platforms is a plus.
Previous professional MLOps experience is not required.

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What You'll Gain

Hands-on experience shipping machine-learning models to production, not just theory.
Practical experience with training pipelines, model deployment, and monitoring.
Understanding of how AI products stay reliable at scale.
Practical exposure to Docker, CI/CD, model serving, and experiment tracking.
Portfolio-ready MLOps projects built on realistic use cases.
Personalized AI feedback on your work.
Professional certificate upon successful completion.
Mentorship and weekly learning sessions.
Opportunity to strengthen your portfolio, resume, and interview readiness.

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Internship Details

Role: MLOps Intern
\n Location: Remote
\n Type: Internship (Unpaid)
\n Level: Entry Level
\n Duration: 6 Weeks
\n Commitment: Flexible (approximately 10–15 hours per week)

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If you love the engineering side of AI and want to learn how real machine-learning systems are built, deployed, and kept running, we'd love to hear from you.

⚠️ Apply through: 👉 www.owlera.site

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